def plot_action_fromdb():
conn = uo.connect('all')
with conn:
df_action = pd.read_sql('SELECT * FROM EUAS_ecm', conn)
df_pro = pd.read_sql('SELECT * FROM EUAS_ecm_program', conn)
df_bs = pd.read_sql('SELECT * FROM EUAS_monthly_weather', conn)
bs_dict = dict(zip(df_bs['Building_Number'], df_bs['ICAO']))
df_action = df_action[['Building_Number', 'high_level_ECM', 'detail_level_ECM', 'Substantial_Completion_Date']]
df_act = df_action.copy()
df_act = df_act.dropna()
df_act['action'] = df_act.apply(lambda r: r['high_level_ECM'] + ' -- ' + r['detail_level_ECM'], axis=1)
df_act.drop(['high_level_ECM', 'detail_level_ECM'], axis=1,
inplace=True)
gr = df_act.groupby('Building_Number')
names = list(gr.groups)
# print names.index('WA0120BN')
lines = ['Building_Number,Time,Action,Electric_Saving,Gas_Saving,Electric_Before,Electric_After,Gas_Before,Gas_After,Electric_CVRMSE,Gas_CVRMSE']
# FIXME: PA0060ZZ has None in eui_gas
del names[153]
# names = ['CA0306ZZ']
for i, name in enumerate(names):
print i, name, '222222222222222222222222222222'
group = gr.get_group(name)
df_temp = group.groupby(['Substantial_Completion_Date'])['action'].apply(lambda x: '\n'.join(x))
df_show = df_temp.to_frame('ECM action')
# df_show['Building Number'] = 'CT0013ZZ'
df_show.reset_index(inplace=True)
df_show['Substantial_Completion_Date'] = pd.to_datetime(df_show['Substantial_Completion_Date'])
df_show.sort('Substantial_Completion_Date', inplace=True)
days_diff = useq.dist_between_adjacent(df_show['Substantial_Completion_Date'].tolist())
pair = zip(df_show['Substantial_Completion_Date'].tolist(),
df_show['ECM action'].tolist())
str_pair = [('{0}-{1}-{2}'.format(x[0].year, x[0].month, x[0].day), x[1]) for x in pair]
breakpoints = [x[0] for x in str_pair]
print breakpoints
assert(len(breakpoints) > 0)
ranges = ['before {0}'.format(breakpoints[0])] + useq.merge_adjacent(breakpoints, lambda x, y: '{0} -- {1}'.format(x, y)) + ['after {0}'.format(breakpoints[-1])]
actionpoints = [x[1] for x in str_pair]
actions = ['pre {0}'.format(actionpoints[0])] + useq.merge_adjacent(actionpoints, lambda x, y: 'post {0} pre {1}'.format(x, y)) + ['post {0}'.format(actionpoints[-1])]
b = name
# df_eng = gr_energy.get_group(b)
# df_eng.reset_index(inplace=True)
s = bs_dict[b]
ar_pair = zip(actions, ranges)
# un-comment if need to plot energy and dd trend side by side
# plot_trend_fromdb(b, s, breakpoints)
print 'plot trend per dd'
plot_trend_per_dd_fromdb(b, s, breakpoints)
results = []
for a, r in zip(actions, ranges):
result = ltm.lean_temperature_fromdb(b, s, 2, r, action=a)
if result == None:
result = (None, None, None)
d = {'building': b, 'station': s, 'timerange': r, 'action': a}
result += tuple([d])
results.append(result)
length = len(results)
if length == 0:
print 'no lean plot generated'
continue
def merge_action(string):
string = string.replace('\n', ';')
string = string.replace('pre ', '')
return string
def concat(string):
return string.replace('\n', ';')
for i in range(len(results) - 1):
d_save, d_cvrmse = plot_saving_fromdb(b, s, results[i], results[i + 1])
lines.append(','.join(map(str, [b, breakpoints[i],
# (merge_action(actions[i])),
concat(actionpoints[i]),
d_save['elec_percent'],
d_save['gas_percent'],
d_save['elec_before'],
d_save['elec_after'],
d_save['gas_before'],
d_save['gas_after'],
d_cvrmse['elec'],d_cvrmse['gas']])))
process_html(b, s, results, breakpoints)
with open (os.getcwd() + '/plot_FY_weather/html/table/action_saving.csv', 'w+') as wt:
wt.write('\n'.join(lines))
def plot_action():
df = pd.read_csv(homedir + 'master_table/ECM/EUAS_ecm.csv')
df = df[['Building Number', 'high_level_ECM', 'detail_level_ECM', 'Substantial Completion Date']]
df_bs = pd.read_csv(homedir + 'master_table/indicator_wECM_weather.csv')
df_bs = df_bs[df_bs['Valid Weather Data'] == 1]
bs_dict = dict(zip(df_bs['Building Number'], df_bs['ICAO']))
print len(df)
df.dropna(inplace=True)
print len(df)
df['action'] = df.apply(lambda r: r['high_level_ECM'] + ' -- ' + r['detail_level_ECM'], axis=1)
df.drop(['high_level_ECM', 'detail_level_ECM'], axis=1,
inplace=True)
gr = df.groupby('Building Number')
names = list(gr.groups)
# print names.index('WA0120BN')
# names = ['CA0168ZZ']
for i, name in enumerate(names[64:]):
print i, name, '222222222222222222222222222222'
group = gr.get_group(name)
df_temp = group.groupby(['Substantial Completion Date'])['action'].apply(lambda x: '\n'.join(x))
df_show = df_temp.to_frame('ECM action')
# df_show['Building Number'] = 'CT0013ZZ'
df_show.reset_index(inplace=True)
df_show['Substantial Completion Date'] = pd.to_datetime(df_show['Substantial Completion Date'])
df_show.sort('Substantial Completion Date', inplace=True)
days_diff = useq.dist_between_adjacent(df_show['Substantial Completion Date'].tolist())
pair = zip(df_show['Substantial Completion Date'].tolist(),
df_show['ECM action'].tolist())
str_pair = [('{0}-{1}-{2}'.format(x[0].year, x[0].month, x[0].day), x[1]) for x in pair]
breakpoints = [x[0] for x in str_pair]
print breakpoints
assert(len(breakpoints) > 0)
ranges = ['before {0}'.format(breakpoints[0])] + useq.merge_adjacent(breakpoints, lambda x, y: '{0} -- {1}'.format(x, y)) + ['after {0}'.format(breakpoints[-1])]
actionpoints = [x[1] for x in str_pair]
actions = ['pre {0}'.format(actionpoints[0])] + useq.merge_adjacent(actionpoints, lambda x, y: 'post {0} pre {1}'.format(x, y)) + ['post {0}'.format(actionpoints[-1])]
b = name
if b in bs_dict:
s = bs_dict[b]
else:
print 'no weather data'
continue
ar_pair = zip(actions, ranges)
# FIXME populate energy and weather data
df_eng = pd.read_csv(weatherdir + \
'energy_temp/{0}_{1}.csv'.format(b, s))
# un-comment if need to plot energy and dd trend side by side
plot_trend(b, s, df_eng, breakpoints)
print 'plot trend per dd'
plot_trend_per_dd(b, s, df_eng, breakpoints)
results = []
for a, r in zip(actions, ranges):
result = ltm.lean_temperature(b, s, 2, r, action=a)
if result == None:
result = (None, None, None)
d = {'building': b, 'station': s, 'timerange': r, 'action': a}
result += tuple([d])
results.append(result)
length = len(results)
if length == 0:
print 'no lean plot generated'
continue
for i in range(len(results) - 1):
plot_saving(b, s, results[i], results[i + 1])
process_html(b, s, results)
def plot_action():
df = pd.read_csv(homedir + 'master_table/ECM/EUAS_ecm.csv')
df = df[[
'Building Number', 'high_level_ECM', 'detail_level_ECM',
'Substantial Completion Date'
]]
df_bs = pd.read_csv(homedir + 'master_table/indicator_wECM_weather.csv')
df_bs = df_bs[df_bs['Valid Weather Data'] == 1]
bs_dict = dict(zip(df_bs['Building Number'], df_bs['ICAO']))
print len(df)
df.dropna(inplace=True)
print len(df)
df['action'] = df.apply(
lambda r: r['high_level_ECM'] + ' -- ' + r['detail_level_ECM'], axis=1)
df.drop(['high_level_ECM', 'detail_level_ECM'], axis=1, inplace=True)
gr = df.groupby('Building Number')
names = list(gr.groups)
# print names.index('WA0120BN')
# names = ['CA0168ZZ']
for i, name in enumerate(names[64:]):
print i, name, '222222222222222222222222222222'
group = gr.get_group(name)
df_temp = group.groupby(['Substantial Completion Date'
])['action'].apply(lambda x: '\n'.join(x))
df_show = df_temp.to_frame('ECM action')
# df_show['Building Number'] = 'CT0013ZZ'
df_show.reset_index(inplace=True)
df_show['Substantial Completion Date'] = pd.to_datetime(
df_show['Substantial Completion Date'])
df_show.sort('Substantial Completion Date', inplace=True)
days_diff = useq.dist_between_adjacent(
df_show['Substantial Completion Date'].tolist())
pair = zip(df_show['Substantial Completion Date'].tolist(),
df_show['ECM action'].tolist())
str_pair = [('{0}-{1}-{2}'.format(x[0].year, x[0].month,
x[0].day), x[1]) for x in pair]
breakpoints = [x[0] for x in str_pair]
print breakpoints
assert (len(breakpoints) > 0)
ranges = ['before {0}'.format(breakpoints[0])] + useq.merge_adjacent(
breakpoints, lambda x, y: '{0} -- {1}'.format(x, y)) + [
'after {0}'.format(breakpoints[-1])
]
actionpoints = [x[1] for x in str_pair]
actions = ['pre {0}'.format(actionpoints[0])] + useq.merge_adjacent(
actionpoints, lambda x, y: 'post {0} pre {1}'.format(x, y)) + [
'post {0}'.format(actionpoints[-1])
]
b = name
if b in bs_dict:
s = bs_dict[b]
else:
print 'no weather data'
continue
ar_pair = zip(actions, ranges)
# FIXME populate energy and weather data
df_eng = pd.read_csv(weatherdir + \
'energy_temp/{0}_{1}.csv'.format(b, s))
# un-comment if need to plot energy and dd trend side by side
plot_trend(b, s, df_eng, breakpoints)
print 'plot trend per dd'
plot_trend_per_dd(b, s, df_eng, breakpoints)
results = []
for a, r in zip(actions, ranges):
result = ltm.lean_temperature(b, s, 2, r, action=a)
if result == None:
result = (None, None, None)
d = {'building': b, 'station': s, 'timerange': r, 'action': a}
result += tuple([d])
results.append(result)
length = len(results)
if length == 0:
print 'no lean plot generated'
continue
for i in range(len(results) - 1):
plot_saving(b, s, results[i], results[i + 1])
process_html(b, s, results)